
from datetime import timedelta

from jms.ods.tms.yl_tms_branch_time_shift_pre import jms_ods__yl_tms_branch_time_shift_pre
# from utils.operators.cluster_for_spark_sql_operator import SparkSqlOperator
from utils.operators.cluster_for_spark_sql_operator import SparkSqlOperator

jms_dim__dim_yl_tms_branch_time_shift_pre_base = SparkSqlOperator(
    task_id='jms_dim__dim_yl_tms_branch_time_shift_pre_base',
    task_concurrency=1,
    pool_slots=3,
    master='yarn',
    name='jms_dim__dim_yl_tms_branch_time_shift_pre_base_{{ execution_date | date_add(1) | cst_ds }}',
    sql='jms/dim/dim_yl_tms_branch_time_shift_pre_base/execute.sql',
    executor_cores=2,
    executor_memory='4G',
    email=['rabie.zhuang@jtexpress.com','yl_bigdata@yl-scm.com'],
    num_executors=4,  # spark.dynamicAllocation.enabled 为 True 时，num_executors 表示最少 Executor 数
    conf={'spark.dynamicAllocation.enabled'                  : 'true',  # 动态资源开启
          'spark.shuffle.service.enabled'                    : 'true',  # 动态资源 Shuffle 服务开启
          # #'spark.dynamicAllocation.maxExecutors'             : 50,  # 动态资源最大扩容 Executor 数
          'spark.dynamicAllocation.maxExecutors'             : 11,  # 动态资源最大扩容 Executor 数
          'spark.dynamicAllocation.cachedExecutorIdleTimeout': 60,  # 动态资源自动释放闲置 Executor 的超时时间(s)
          'spark.sql.sources.partitionOverwriteMode'         : 'dynamic',  # 允许删改已存在的分区
          'spark.executor.memoryOverhead'                    : '1G',  # 堆外内存
          'spark.sql.shuffle.partitions'                     : 20,
          },
    yarn_queue='pro',
    execution_timeout=timedelta(minutes=30),
)

jms_dim__dim_yl_tms_branch_time_shift_pre_base << jms_ods__yl_tms_branch_time_shift_pre
